1. Field
The present invention relates generally to data interpretation and, more specifically, to diagnostic techniques using particle analyzers, such as flow cytometer.
2. Description of the Background Art
Flow cytometry is a technique that is commonly employed to analyze individual particles in a sample in order to detect characteristics of the particles. A flow cytometry device performs analysis on a single particle at a time in order to determine information about the sample, including concentrations, percentages, positional parameters, and shape parameters, among other characteristics.
Hematology instruments commonly implement flow cytometry in order to aid in the detection of abnormalities in a given blood sample. Such abnormalities are often indicative of disease, and therefore it is important that hematology instruments provide consistently useful results.
Various methods can be employed by flow cytometry devices to perform multiparametric analysis of individual particles, such as blood cells in the case of hematology instruments, the results of which are then aggregated to produce the characteristic data for the blood sample. For example, the volume of a cell can be determined indirectly by applying direct current to the cell suspended in a conductive diluent, resulting in a change in electrical resistance based on the volume of the cell. Additional parameters that may be used to interrogate a cell may include conductivity measurements using radio frequencies, as well as light scatter parameters using a laser. The types of measurements that can be used to determine characteristics of cells is constantly expanding as the field continues to evolve, and instruments implementing flow cytometry are improved to generate additional parametric data.
As noted, one of the ways to interpret blood sample characteristics is through the use of shape parameters. Shape parameters are used to characterize the distribution of data on a two-dimensional histogram. A typical method for determining a shape parameter for a blood sample is to determine the standard deviation based on the given measurement data. For example, a population, or sample, of blood cells is tested in a flow cytometry device to generate scalar values representing two features of each individual cell. These two features may be, again for example, the volume and conductivity of the cell. In order to generate data that can be easily used to flag suspect blood samples, the standard deviation of the population is determined based on the aggregate of volume and conductivity data for many cells.
Although standard deviation data for flow cytometry results is often useful in diagnosis, it may have limitations which can hamper effective clinical study. Despite best efforts, data from any instrumentation is prone to noise. This can result in skewing of the calculated standard deviation, deteriorating its usefulness. If two populations have otherwise identical histograms, except the first has an outlier event caused by noise that the second population does not, the standard deviation of the two samples may be significantly different. However, since the two populations have otherwise identical histograms, it would be useful to determine a shape parameter that indicates this similarity.
Moreover, since standard deviation is a scalar value, it cannot capture the intricacies associated with multivariate descriptions of the population characteristics. Two populations having entirely different histograms over two parameters may nevertheless have similar or identical standard deviations.
Accordingly, what is desired is a parameter capable of providing detailed shape information without being significantly impacted by noise.